Visual tag emerging pattern detection

ABSTRACT

Systems, devices, media, and methods are presented for identifying emerging viewing patterns for visual media such as still images and videos. Emerging viewing patterns are identified by identifying visual tags for visual media viewed by users, selecting a subset of the tags by applying a taxonomy-based filter, generating pattern candidates from the subset, evaluating consumption metrics for each of the generated patterns, and ranking the generated pattern candidates responsive to the consumption metrics to identify emerging viewing patterns for the users.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation of U.S. application Ser. No.17/828,827 filed on May 31, 2022, which is a Continuation of U.S.application Ser. No. 17/120,933 filed on Dec. 14, 2020, now U.S. Pat.No. 11,393,203, the contents of which are incorporated fully herein byreference.

TECHNICAL FIELD

Examples set forth in this disclosure relate generally to applicationsrunning on client devices and server systems supporting those devices.More particularly, but not by way of limitation, this disclosureaddresses systems and methods for identifying emerging visual mediaviewing patterns, e.g., to tailor content and advertising to viewers.

BACKGROUND

Visual media such as still images and video are routinely viewed byusers on electronic devices. The visual media is typically stored by aserver system and sent to the electronic devices of the users forviewing.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosed subject matter is best understood from the followingdetailed description when read in connection with the accompanyingdrawings, with like elements having the same reference numerals. When aplurality of similar elements is present, a single reference numeral maybe assigned to the plurality of similar elements with a small letterdesignation referring to specific elements. When referring to theelements collectively or to a non-specific one or more of the elements,the small letter designation may be dropped. To easily identify thediscussion of any particular element or act, the most significant digitor digits in a reference number refer to the figure number in which thatelement is first introduced. This emphasizes that according to commonpractice, the various features of the drawings are not drawn to scaleunless otherwise indicated. On the contrary, the dimensions of thevarious features may be expanded or reduced for clarity. Included in thedrawings are the following figures:

FIG. 1 is a block diagram of an example emerging pattern detectionsystem in an online client-server system.

FIG. 2A is a block diagram illustrating an example emerging patterndetection system.

FIG. 2B is a block diagram illustrating example components forimplementing emerging pattern detection for a client device.

FIG. 3 is a block diagram illustrating server and client components forimplementing emerging pattern detection.

FIG. 4 is a flow diagram illustrating an example method for implementingemerging pattern detection on a server system.

FIG. 5 is a flow diagram illustrating an example method for gatheringdata on a client device for implementing emerging pattern detection onthe server system.

FIG. 6 is a flow diagram illustrating an example method for generatingpattern candidates for use in the example method of FIG. 4 .

FIG. 7 is a flow diagram illustrating an example method for evaluatingpattern candidates for use in the example method of FIG. 4 .

FIG. 8 is a diagrammatic representation of an example hardwareconfiguration for a client device embodied as a mobile device.

FIG. 9 is a diagrammatic representation of a machine in the form of acomputer system within which a set of instructions may be executed forcausing the machine to perform any one or more of the methodologiesdescribed herein, in accordance with some examples.

FIG. 10 is block diagram showing a software architecture within whichaspects of the present disclosure may be implemented, in accordance withsome examples.

DETAILED DESCRIPTION

Aspects of the subject matter disclosed herein are directed toidentifying emerging viewing patterns for visual media (e.g., stillimages or video) using visual tags. In social media platforms, usersproduce several sorts of content that become available for consumptionby other users. The consumption may change over time according to theusers' cohorts (i.e., age group, gender), location, or external events(i.e., political, economic, cultural). Understanding topics that viewersare engaging is useful for (i) recommending new content, (ii) retainingusers, and (iii) finding new partnerships. Instead of looking at allvisual tags available through computer vision detection individually,filtering techniques are applied in order to select more specific visualtags (e.g., only leaf nodes in a taxonomy) and combinations of thoseselected tags are evaluated to better identify emerging viewing patternsand trends.

The description that follows includes systems, methods, techniques,instruction sequences, and computing machine program productsillustrative of examples of the disclosure. In the followingdescription, for the purposes of explanation, numerous specific detailsare set forth in order to provide an understanding of various examplesof the disclosed subject matter. It will be evident, however, to thoseskilled in the art, that examples of the disclosed subject matter may bepracticed without these specific details. In general, well-knowninstruction instances, protocols, structures, and techniques are notnecessarily shown in detail.

In accordance with one example, a method is provided for identifyingemerging viewing patterns for visual media (e.g., still images or video)viewed by groups of users. The method identifies emerging viewingpatterns by identifying visual tags for each of a plurality of visualmedia viewed by a first of the groups of users, selecting a subset ofthe identified visual tags by applying a taxonomy-based filter,generating pattern candidates from the selected subset, each of thepattern candidates including two or more of the identified visual tagsfrom the subset of the identified visual tags, evaluating consumptionmetrics of the visual media by the first group of users for each of thegenerated pattern candidates, and ranking the generated patterncandidates responsive to the evaluated consumption metrics to identifyemerging viewing patterns for the first group of users.

In accordance with another example, a system is provided for identifyingemerging viewing patterns. The system includes a processor. Theprocessor is configured to identify visual tags for each of a pluralityof visual media viewed by a first of the groups of users, select asubset of the identified visual tags by applying a taxonomy-basedfilter, generate pattern candidates from the selected subset, each ofthe pattern candidates including two or more of the identified visualtags from the subset of the identified visual tags, evaluate consumptionmetrics of the visual media by the first group of users for each of thegenerated pattern candidates, and rank the generated pattern candidatesresponsive to the evaluated consumption metrics to identify emergingviewing patterns for the first group of users.

In accordance with another example, a non-transitory processor-readablestorage medium is provided that stores processor-executable instructionsthat, when executed by a processor of a machine, cause the machine toperform operations to identify emerging viewing patterns of visualmedia. The operations performed by the machine include identifyingvisual tags for each of a plurality of visual media viewed by a first ofthe groups of users, selecting a subset of the identified visual tags byapplying a taxonomy-based filter, generating pattern candidates from theselected subset, each of the pattern candidates including two or more ofthe identified visual tags from the subset of the identified visualtags, evaluating consumption metrics of the visual media by the firstgroup of users for each of the generated pattern candidates, and rankingthe generated pattern candidates responsive to the evaluated consumptionmetrics to identify emerging viewing patterns for the first group ofusers.

FIG. 1 is a block diagram illustrating a system 100, according to someexamples, configured to identify emerging viewing patterns of users. Thesystem 100 includes one or more client devices such as client device110. The client device 110 includes, but is not limited to, a mobilephone, desktop computer, laptop, portable digital assistants (PDA),smart phone, tablet, ultrabook, netbook, laptop, multi-processor system,microprocessor-based or programmable consumer electronic, game console,set-top box, computer in a vehicle, or any other communication devicethat a user may utilize to access the system 100. In some examples, theclient device 110 includes a display module (not shown) to displayinformation (e.g., in the form of user interfaces). In further examples,the client device 110 includes one or more of touch screens,accelerometers, gyroscopes, cameras, microphones, global positioningsystem (GPS) devices, and so forth. The client device 110 may be adevice of a user that is used to access and utilize an online socialplatform. For example, the client device 110 may be used to inputinformation to create an account, exchange information over a network102, and so forth.

In one example, client device 110 is a device of a user who is using asocial media application on the device. Client device 110 may call aserver for a social platform (e.g., hosted by server system 108) via thesocial media application directly or through one or more third-partyservers 128 (e.g., utilizing one or more third-party applications 130).Application server 104 tracks visual media provided to the client device110 and corresponding viewing statistics gathered from the client device110 (e.g., duration of video, time spent watching video, number of timeimage is viewed, etc.) as a dataset in database 126. By analyzing thedataset using techniques disclosed herein, the application server 104 isable to automatically detect viewing patterns/preferences and to delivervisual media to the client device in accordance with the viewingpatterns/preferences.

One or more users may be a person, a machine, or other means ofinteracting with the client device 110. In examples, the user may not bepart of the system 100 but may interact with the system 100 via theclient device 110 or other means. For instance, the user may provideinput (e.g., touch screen input or alphanumeric input) to the clientdevice 110 and the input may be communicated to other entities in thesystem 100 (e.g., third-party servers 128, server system 108, etc.) viathe network 102. In this instance, the other entities in the system 100,in response to receiving the input from the user, may communicateinformation to the client device 110 via the network 102 to be presentedto the user. In this way, the user interacts with the various entitiesin the system 100 using the client device 110.

The system 100 further includes a network 102. One or more portions ofnetwork 102 may be an ad hoc network, an intranet, an extranet, avirtual private network (VPN), a local area network (LAN), a wirelessLAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), ametropolitan area network (MAN), a portion of the Internet, a portion ofthe public switched telephone network (PSTN), a cellular telephonenetwork, a wireless network, a WiFi network, another type of network, ora combination of two or more such networks.

The client device 110 may access the various data and applicationsprovided by other entities in the system 100 via a web client 112 (e.g.,a browser) and/or one or more client applications 114. The client device110 may include one or more client application(s) 114 (also referred toas “apps”) such as, but not limited to, a web browser, messagingapplication, electronic mail (email) application, an e-commerce siteapplication, a mapping or location application, and the like. The clientapplication(s) may also display visual media on a display of the clientdevice 110, gather viewing data for visual media viewed by the user ofthe electronic device, and generate viewing consumption metrics.

In some examples, one or more client application(s) 114 are included ina given one of the client device 110, and configured to locally providethe user interface and at least some of the functionalities, with theclient application(s) 114 configured to communicate with other entitiesin the system 100 (e.g., third-party server(s) 128, server system 108,etc.), on an as-needed basis, for data processing capabilities notlocally available (e.g., to access location information, to authenticatea user, etc.). Conversely, one or more client application(s) 114 may notbe included in the client device 110, and then the client device 110 mayuse its web browser to access the one or more applications hosted onother entities in the system 100 (e.g., third-party server(s) 128,server system 108, etc.).

A server system 108 provides server-side functionality via the network102 (e.g., the Internet or wide area network (WAN)) to: one or morethird party server(s) 128, and one or more client devices 110. Theserver system 108 includes an application program interface (API) server120, a web server 122, and a pattern detection system 124, that may becommunicatively coupled with one or more database(s) 126. The one ormore database(s) 126 may be storage devices that store data (e.g., in adataset) related to users of the server system 108, applicationsassociated with the server system 108, viewing consumption metrics(e.g., based on demographic information), cloud services, housing marketdata, and so forth. The one or more database(s) 126 may further storeinformation related to third party server(s) 128, third-partyapplication(s) 130, client device 110, client application(s) 114, users,and so forth. In one example, the one or more database(s) 126 may becloud-based storage.

The server system 108 may be a cloud computing environment, according tosome examples. The server system 108, and any servers associated withthe server system 108, may be associated with a cloud-based application.In one example the server system 108 includes a pattern detection system124 for detecting emerging patterns in visual media viewing. Patterndetection system 124 may include one or more servers and may beassociated with a cloud-based application. Pattern detection system 124may distribute visual metric applications to gather and store viewingmetrics to client devices (e.g., as part of a social medium applicationor update thereto), obtain viewing metrics from the distributed apps onthose client devices, and store those results in a database(s) 126. Thepattern detection system 124 analyzes a dataset including the viewingmetrics from the client devices to identify emerging viewing patterns.

The system 100 further includes one or more third party server(s) 128.The one or more third-party server(s) 128 may include one or morethird-party application(s) 130. The one or more third-partyapplication(s) 130, executing on third party server(s) 128 may interactwith the server system 108 via API server 120 via a programmaticinterface provided by the API server 120. For example, one or more ofthe third-party applications 130 may request and utilize informationfrom the server system 108 via the API server 120 to support one or morefeatures or functions on a website hosted by the third party or anapplication hosted by the third party.

FIG. 2A is a block diagram illustrating an example of the patterndetection system 124. The pattern detection system 124 includes a visualtag engine 202, a taxonomy engine 204, a candidate generator 206, ametric evaluation engine 208, and a viewing metric database 210. Theviewing metric database 210 is stored in the database 126 of the serversystem 108. The device capabilities collection engine 202 and the deviceinference engine runs on the application server 104 of the server system108. The visual tag engine 202, taxonomy engine 204, candidate generator206, and metric evaluation engine 208 run on the server system 108.

The visual tag engine 202 in device capabilities collection engine 202is configured to analyze visual media and generate tags corresponding toidentified objects (e.g., sun, water, boy, girl, etc.) in the visualmedia. In one example, the process of generating tags is implementedusing a machine-learning algorithm that compares the visual media to adatabase trained using a collection of tagged images.

Machine learning refers to an algorithm that improves incrementallythrough experience. By processing a large number of different inputdatasets, a machine-learning algorithm can develop improvedgeneralizations about particular datasets, and then use thosegeneralizations to produce an accurate output or solution whenprocessing a new dataset. Broadly speaking, a machine-learning algorithmincludes one or more parameters that will adjust or change in responseto new experiences, thereby improving the algorithm incrementally; aprocess similar to learning.

In the context of computer vision, mathematical models attempt toemulate the tasks accomplished by the human visual system, with the goalof using computers to extract information from an image and achieve anaccurate understanding of the contents of the image. Computer visionalgorithms have been developed for a variety of fields, includingartificial intelligence and autonomous navigation, to extract andanalyze data in digital images and video.

Deep learning refers to a class of machine-learning methods that arebased on or modeled after artificial neural networks. An artificialneural network is a computing system made up of a number of simple,highly interconnected processing elements (nodes), which processinformation by their dynamic state response to external inputs. A largeartificial neural network might have hundreds or thousands of nodes.

A convolutional neural network (CNN) is a type of neural network that isfrequently applied to analyzing visual images, including digitalphotographs and video. The connectivity pattern between nodes in a CNNis typically modeled after the organization of the human visual cortex,which includes individual neurons arranged to respond to overlappingregions in a visual field. A neural network that is suitable for use inthe determining process described herein is based on one of thefollowing architectures: VGG16, VGG19, ResNet50, Inception V3, Xception,or other CNN-compatible architectures.

The taxonomy engine 204 selects a subset of the identified visual tagsby applying a taxonomy-based filter. The candidate generator 206generates pattern candidates from the selected subset. In one example,each of the pattern candidates includes two or more of the identifiedvisual tags from the subset of the identified visual tags. The metricevaluation engine 208 evaluates consumption metrics of the visual mediaby the first group of users for each of the generated pattern candidatesand ranks the generated pattern candidates based on the evaluatedconsumption metrics to identify emerging viewing patterns.

FIG. 2B is a block diagram illustrating an example of the clientapplications 114 for use in identifying emerging viewing patterns. Theseclient applications 114 include a viewing metrics recorder 212 and aviewing metric database 214. The viewing metrics recorder 212 monitorsviewing of visual media on an electronic device and stores one or moreparameters associated with each of the visual media (e.g., how long orhow many times it was viewed). The viewing metrics database 214 storesthe recorded viewing metrics (e.g., for transmission to the serversystem 108 for analysis).

FIG. 3 depicts an example of components of the server system 108 and theclient device 110 for implementing emerging pattern detection. A videoserver 306 communicates with the metric evaluation engine 208 and agateway 318 (e.g., a gateway for a full service network such as theInternet) when scheduling visual media to serve to client device 110.The video server 306 notifies the metric evaluation engine 208 of thevisual media that is being scheduled. Additionally, the video server 306may determine visual media to next serve based on input from the metricevaluation engine 208 once sufficient data is available to detect theemerging viewing patterns of the user of the electronic device 110.

Visual media (e.g., videos 314) received by the electronic device 110are routed to a video controller 310 for display on a display of theelectronic device 110. The video controller 310 receives instructionsfrom user of the electronic device 110 (via a user interface such as atouchscreen) to control playback of the visual media. The videocontroller 310 tracks data associated with the playback (e.g., time ofviewing, total viewing time, number of times viewed etc.) and stores thedata in the viewing metrics database 214.

FIG. 4 is a flow diagram illustrating an example method 400 forexecution by a server system (e.g., server system 108) to detectemerging viewing patterns, FIG. 5 is a flow diagram illustrating anexample method 500 for execution by an electronic device (e.g., clientdevice 110) to gather viewing metrics, FIG. 6 is a flow diagramillustrating an example method 600 for generating pattern candidates foruse in the example method of FIG. 4 , and FIG. 7 is a flow diagramillustrating an example method 700 for evaluating pattern candidates foruse in the example method of FIG. 4 .

Although the below description of the methods refers to the patterndetection system 124 running on server system 108 and applications 114on client devices 110, other systems and devices for identifyingemerging patterns will be understood from the description herein.Although the flowcharts may describe the operations as a sequentialprocess, many of the operations can be performed in parallel orconcurrently. In addition, the order of the operations may bere-arranged. A process is terminated when its operations are completed.A process may correspond to a method, a procedure, etc. The steps of amethods may be performed in whole or in part, may be performed inconjunction with some or all of the steps in other methods, and/or maybe performed by any number of different systems, such as the systemsdescribed in FIGS. 1, 2A, 2B, 3, and 8-10 .

At block 402, the server system 108 distributes a viewing metricsapplication 114 to user devices. The viewing metrics application isconfigured for execution on the electronic devices 110, gatheringviewing metrics for visual media presented on the electronic device, andreturning viewing metrics to the server system 108 for pattern analysisto identify emerging viewing patterns. The visual media may includestill images, videos, or a combination thereof.

At block 404, the server system 108 receives the viewing metrics fromthe electronic devices 110. Server system 108 may receive the viewingmetrics from the client applications 114 on the electronic device 110via the network 102.

At block 406, the server system 108 identifies visual tags for visualmedia viewed by the electronic devices 110 by users. The visual tagsidentify objects in each of the visual media being served to the clientdevices 110. For example, a first video or still image may be tagged asincluding a person, a boy, and a clothing object and a second video orstill image may be tagged as including a person, a girl, an animal, anda dog object. In one example, the tagged elements for each piece ofvisual media is determined using an object detection machine learningmodel and the determined objects are stored along with an identifier forthe visual media including those objects in a database (e.g., database126).

At block 408, the server system 108 selects a subset of the identifiedvisual tags (block 408) by applying a taxonomy-based filter. Thetaxonomy-based filter may include a hierarchical tree structure. In oneexample, level one (1) of the hierarchical tree structure is a broadcategory, e.g., real objects, level two (2) is a first subset, e.g.,person, clothing, etc., level three (3) is a second subset, e.g., boyunder person, and shorts under clothing. The server system selects thesubset of the identified visual tags from the identified visual tagslocated at one or more specific levels in a taxonomy of thetaxonomy-based filter. In one example, the server system 108 onlyselects visual tags from one or more specific abstraction levels (e.g.,level 2 and level 3 as other levels may be too general or too specificin detecting meaningful viewing patterns). In accordance with thisexample, one or more other levels may be selected depending on thetaxonomy. In another example, the server system 108 only selects tagsfrom levels having a specific characteristic (e.g., leaf nodes; i.e., anode with no subset nodes). Other selection techniques, includingcombinations of one or more of the provided examples, may be utilized toselect visual tags and are considered within the scope of the presentinvention.

At block 410, the server system 110 generates pattern candidates. In oneexample, each of the pattern candidates including two or more of theidentified visual tags from the subset of the identified visual tags. Inone example, the server system generates the pattern candidates bygenerating all combinations of the identified visual tags in the subsetof the identified visual tags having a number, N, of visual tags, whereN is equal or greater than 2 (block 602). In one implementation of thisexample, all generated combinations are used as pattern candidates. Inanother implementation, one or more combinations are removed from thegenerated combinations to derive the pattern candidates. For example,the system may identify prohibited combinations within the generatedcombinations (e.g., those having a parent-child relationship such ashoes and sandals) within the taxonomy of the taxonomy-based filter(block 604) and omit those prohibited combinations from the patterncandidates (block 606).

At block 412, the server system 110 evaluates viewing metrics for eachof the generated pattern candidates. The server system may evaluateconsumption metrics of the visual media by the first group of users(e.g., a group of users with similar characteristics such as age group,gender, etc.) for each of the generated pattern candidates. In oneexample, the server system 110 evaluates the consumption metrics byreceiving one or more consumption metrics from each of the first groupof users (block 702), scoring each of the generated pattern candidatesresponsive to the received consumption metrics (block 706; e.g., bycalculating a ratio of an average percentage of viewing time out of anassociated length of time that a group viewed each visual media), andaggregate the scoring (block 706; e.g., aggregate the calculated ratiofor each pattern candidate).

For a video (which has an associated length of time), the consumptionmetrics may include total viewing time and the scoring may includecalculating a ratio of an average percentage of time out of theassociated length of time that the first group viewed each of the videosand aggregating, for each pattern candidate, the ratio of the averagepercentage of time out of the associated length of time that the firstgroup viewed each of the videos. For a still image, the consumptionmetrics may include a number of time viewed or shared and the scoringmay include calculating a ratio of the total number of times viewed orshared to the total number of unique images viewed by the first groupand aggregating, for each pattern candidate, the ratio of the averagenumber of times viewed or shared out of the total number of uniqueimages the first group viewed.

In one example, a z-score is generated that describes the differencebetween the consumption metric in the target pattern and the otherpatterns. In accordance with this example, the consumption metric may bebased on a combination of two content consumption metrics for a video(e.g., a sum of view time ratio (or VTR) for all videos with a specificpattern and the total number of records with a specific pattern. Theview time ratio can be calculated as shown in equation (1):

$\begin{matrix}{{{{VTR}(s)} = {\frac{1}{{size}(V)}{\sum}_{v{in}V}\frac{{view}{time}( {s,v} )}{{video}{duration}( {s,v} )}}},} & (1)\end{matrix}$

where V is the set of viewers of the video, s, and size(V) is the numberof viewers of video, s.

At block 414, the server system 110 ranks the generated patterncandidates. In one example, the generated pattern candidates are rankedin accordance with their z-score. In accordance with this example, ahigher z-score refers to a pattern that is more different from theaverage (normal behavior) and, thus, indicates an emerging pattern and alower z-score indicates a non-emerging pattern.

At block 416, the server system 110 visually presents rankings. In oneexample, the pattern detection system 124 generates a graph for visualpresentation on a display. In accordance with this example, the patterndetection system 124 may select the ten pattern candidates having thehighest z-score and present those candidates in order based on theirz-score along with the objects making up those candidates.

In one example, the rankings (e.g., based on z-score) are validatedusing a multi-fold (e.g., 5-fold) cross validation. In accordance withthis example, assuming a K-fold cross validation, the pattern detectionsystem 124 separates the dataset into K folds, each including adifferent grouping of training and validation sets. For each fold, thepattern detection system 124 uses the training set to find the bestpattern, e.g., based on the highest z-score. The pattern detectionssystem 124 then labels the visual media in the validation set with ahigh score as + and without a high score as −. After labeling the entiredataset with + and −, the pattern detection system compares the userengagement (viewing time, etc.) of group + and − to get the z-score.This process is repeated for each fold to get a median z-score (i.e., anunbiased z-score, which becomes the z-score).

At block 502, an electronic device (e.g., the client device 110)downloads and installs the viewing metric application 114. In oneexample, the client device 110 receives the viewing metric application114 from the server system 108 over the network 102 via the API server120 of the application server 104.

At block 504, the client device 110 executes the viewing metricapplication 114. A processor (e.g., CPU 830; FIG. 8 ) of the clientdevice 110 executes the viewing metric application 114. In one example,the processor generates a viewing metrics database 214 in memory of theclient device 110 for recording viewing metrics associated with visualmedia presented by the client device 110.

At block 506, the client device 110 records the viewing metrics. In oneexample, the processor of the client device 110 gathers viewing metrics(e.g., from the video controller 310) and records the viewing in memory.

At block 508, the client device 110 stores the viewing metrics. In anexample, the client device stores the recorded viewing metrics in aviewing metrics database 214 in the memory of the client device 110.

At block 510, the client device 110 uploads the viewing metrics to theserver system 108. In one example, the client device 110 retrieves theviewing metrics from the viewing metrics database 214 and sends theviewing metrics to the server system 108 over the network 102 via theAPI server 120 of the application server 104.

FIG. 8 is a high-level functional block diagram of an example clientdevice 110 embodied as an example mobile device 890 that includes theviewing metrics recorder 212 and the viewing metrics database 214.Mobile device 890 includes a flash memory 840A which includesprogramming to perform all or a subset of the functions described hereinfor viewing metrics recorder 212 and viewing metrics database 214.Mobile device 890 can include a camera 870 that comprises at least onevisible light camera (e.g., first and second visible light cameras withoverlapping fields of view or a visible light camera and a depth sensorwith substantially overlapping fields of view). Memory 840A may furtherinclude multiple images or video, which are generated via the camera870.

As shown, the mobile device 890 includes an image display 880, an imagedisplay driver 882 to control the image display 880, and a controller884. In the example of FIG. 8 , the image display 880 and a user inputdevice are integrated together into a touch screen display.

Examples of touch screen type mobile devices that may be used include(but are not limited to) a smart phone, a personal digital assistant(PDA), a tablet computer, a laptop computer, or other portable device.However, the structure and operation of the touch screen type devices isprovided by way of example; and the subject technology as describedherein is not intended to be limited thereto. For purposes of thisdiscussion, FIG. 8 therefore provides block diagram illustrations of theexample mobile device 890 having a touch screen display for displayingcontent and receiving user input as (or as part of) the user interface.

As shown in FIG. 8 , the mobile device 890 includes at least one digitaltransceiver (XCVR) 810, shown as WWAN XCVRs, for digital wirelesscommunications via a wide area wireless mobile communication network.The mobile device 890 also includes additional digital or analogtransceivers, such as short range XCVRs 820 for short-range networkcommunication, such as via NFC, VLC, DECT, ZigBee, Bluetooth™, or WiFi.For example, short range XCVRs 820 may take the form of any availabletwo-way wireless local area network (WLAN) transceiver of a type that iscompatible with one or more standard protocols of communicationimplemented in wireless local area networks, such as one of the Wi-Fistandards under IEEE 802.11.

To generate location coordinates for positioning of the mobile device890, the mobile device 890 can include a global positioning system (GPS)receiver. Alternatively, or additionally the mobile device 890 canutilize either or both the short range XCVRs 820 and WWAN XCVRs 810 forgenerating location coordinates for positioning. For example, cellularnetwork, WiFi, or Bluetooth™ based positioning systems can generate veryaccurate location coordinates, particularly when used in combination.Such location coordinates can be transmitted to the eyewear device overone or more network connections via XCVRs 810, 820.

The transceivers 810, 820 (network communication interface) conforms toone or more of the various digital wireless communication standardsutilized by modern mobile networks. Examples of WWAN transceivers 810include (but are not limited to) transceivers configured to operate inaccordance with Code Division Multiple Access (CDMA) and 3rd GenerationPartnership Project (3GPP) network technologies including, for exampleand without limitation, 3GPP type 2 (or 3GPP2) and LTE, at timesreferred to as “4G.” For example, the transceivers 810, 820 providetwo-way wireless communication of information including digitized audiosignals, still image and video signals, web page information for displayas well as web related inputs, and various types of mobile messagecommunications to/from the mobile device 890.

The mobile device 890 further includes a microprocessor, shown as CPU830, sometimes referred to herein as the host controller. A processor isa circuit having elements structured and arranged to perform one or moreprocessing functions, typically various data processing functions.Although discrete logic components could be used, the examples utilizecomponents forming a programmable CPU. A microprocessor for exampleincludes one or more integrated circuit (IC) chips incorporating theelectronic elements to perform the functions of the CPU. The processor830, for example, may be based on any known or available microprocessorarchitecture, such as a Reduced Instruction Set Computing (RISC) usingan ARM architecture, as commonly used today in mobile devices and otherportable electronic devices. Of course, other processor circuitry may beused to form the CPU 830 or processor hardware in smartphone, laptopcomputer, and tablet.

The microprocessor 830 serves as a programmable host controller for themobile device 890 by configuring the mobile device 890 to performvarious operations, for example, in accordance with instructions orprogramming executable by processor 830. For example, such operationsmay include various general operations of the mobile device, as well asoperations related to the programming for the viewing metrics recorder212 and the viewing metrics database 214. Although a processor may beconfigured by use of hardwired logic, typical processors in mobiledevices are general processing circuits configured by execution ofprogramming.

The mobile device 890 includes a memory or storage device system, forstoring data and programming. In the example, the memory system mayinclude a flash memory 840A and a random access memory (RAM) 840B. TheRAM 840B serves as short term storage for instructions and data beinghandled by the processor 830, e.g., as a working data processing memory.The flash memory 840A typically provides longer term storage.

Hence, in the example of mobile device 890, the flash memory 840A isused to store programming or instructions for execution by the processor830. Depending on the type of device, the mobile device 890 stores andruns a mobile operating system through which specific applications,including programming for the viewing metrics recorder 212 and theviewing metrics database 214 are executed. Applications, such as viewingmetrics recorder 212 and the viewing metrics database 214, may be anative application, a hybrid application, or a web application (e.g., adynamic web page executed by a web browser) that runs on mobile device890. Examples of mobile operating systems include Google Android, AppleiOS (I-Phone or iPad devices), Windows Mobile, Amazon Fire OS, RIMBlackBerry operating system, or the like.

FIG. 9 is a diagrammatic representation of a machine 900 within whichinstructions 908 (e.g., software, a program, an application, an applet,an app, or other executable code) for causing the machine 900 to performany one or more of the methodologies discussed herein may be executed.For example, the instructions 908 may cause the machine 900 to executeany one or more of the methods described herein. The instructions 908transform the general, non-programmed machine 900 into a particularmachine 900 programmed to carry out the described and illustratedfunctions in the manner described. The machine 900 may operate as astandalone device or may be coupled (e.g., networked) to other machines.In a networked deployment, the machine 900 may operate in the capacityof a server machine or a client machine in a server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment.

The machine 900 may comprise, but not be limited to, a server computer,a client computer, a personal computer (PC), a tablet computer, a laptopcomputer, a netbook, a set-top box (STB), a PDA, an entertainment mediasystem, a cellular telephone, a smart phone, a mobile device, a wearabledevice (e.g., a smart watch), a smart home device (e.g., a smartappliance), other smart devices, a web appliance, a network router, anetwork switch, a network bridge, or any machine capable of executingthe instructions 908, sequentially or otherwise, that specify actions tobe taken by the machine 900. Further, while only a single machine 900 isillustrated, the term “machine” shall also be taken to include acollection of machines that individually or jointly execute theinstructions 908 to perform any one or more of the methodologiesdiscussed herein.

The machine 900 may include processors 902, memory 904, and I/Ocomponents 942, which may be configured to communicate with each othervia a bus 944. In an example, the processors 902 (e.g., a CentralProcessing Unit (CPU), a Reduced Instruction Set Computing (RISC)processor, a Complex Instruction Set Computing (CISC) processor, aGraphics Processing Unit (GPU), a Digital Signal Processor (DSP), anASIC, a Radio-Frequency Integrated Circuit (RFIC), another processor, orany suitable combination thereof) may include, for example, a processor906 and a processor 910 that execute the instructions 908. The term“processor” is intended to include multi-core processors that maycomprise two or more independent processors (sometimes referred to as“cores”) that may execute instructions contemporaneously. Although FIG.9 shows multiple processors 902, the machine 900 may include a singleprocessor with a single core, a single processor with multiple cores(e.g., a multi-core processor), multiple processors with a single core,multiple processors with multiple cores, or any combination thereof.

The memory 904 includes a main memory 912, a static memory 914, and astorage unit 916, both accessible to the processors 902 via the bus 944.The main memory 904, the static memory 914, and storage unit 916 storethe instructions 908 embodying any one or more of the methodologies orfunctions described herein. The instructions 908 may also reside,completely or partially, within the main memory 912, within the staticmemory 914, within machine-readable medium 918 (e.g., a non-transitorymachine-readable storage medium) within the storage unit 916, within atleast one of the processors 902 (e.g., within the processor's cachememory), or any suitable combination thereof, during execution thereofby the machine 900.

Furthermore, the machine-readable medium 918 is non-transitory (in otherwords, not having any transitory signals) in that it does not embody apropagating signal. However, labeling the machine-readable medium 918“non-transitory” should not be construed to mean that the medium isincapable of movement; the medium should be considered as beingtransportable from one physical location to another. Additionally, sincethe machine-readable medium 918 is tangible, the medium may be amachine-readable device.

The I/O components 942 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 942 that are included in a particular machine will depend onthe type of machine. For example, portable machines such as mobilephones may include a touch input device or other such input mechanisms,while a headless server machine will likely not include such a touchinput device. It will be appreciated that the I/O components 942 mayinclude many other components that are not shown in FIG. 9 . In variousexamples, the I/O components 942 may include output components 928 andinput components 930. The output components 928 may include visualcomponents (e.g., a display such as a plasma display panel (PDP), alight emitting diode (LED) display, a liquid crystal display (LCD), aprojector, or a cathode ray tube (CRT)), acoustic components (e.g.,speakers), haptic components (e.g., a vibratory motor, resistancemechanisms), other signal generators, and so forth. The input components930 may include alphanumeric input components (e.g., a keyboard, a touchscreen configured to receive alphanumeric input, a photo-opticalkeyboard, or other alphanumeric input components), point-based inputcomponents (e.g., a mouse, a touchpad, a trackball, a joystick, a motionsensor, or another pointing instrument), tactile input components (e.g.,a physical button, a touch screen that provides location, force oftouches or touch gestures, or other tactile input components), audioinput components (e.g., a microphone), and the like.

In further examples, the I/O components 942 may include biometriccomponents 932, motion components 934, environmental components 936, orposition components 938, among a wide array of other components. Forexample, the biometric components 932 include components to detectexpressions (e.g., hand expressions, facial expressions, vocalexpressions, body gestures, or eye tracking), measure biosignals (e.g.,blood pressure, heart rate, body temperature, perspiration, or brainwaves), identify a person (e.g., voice identification, retinalidentification, facial identification, fingerprint identification, orelectroencephalogram-based identification), and the like. The motioncomponents 934 include acceleration sensor components (e.g.,accelerometer), gravitation sensor components, rotation sensorcomponents (e.g., gyroscope), and so forth. The environmental components936 include, for example, illumination sensor components (e.g.,photometer), temperature sensor components (e.g., one or morethermometers that detect ambient temperature), humidity sensorcomponents, pressure sensor components (e.g., barometer), acousticsensor components (e.g., one or more microphones that detect backgroundnoise), proximity sensor components (e.g., infrared sensors that detectnearby objects), gas sensors (e.g., gas detection sensors to detectionconcentrations of hazardous gases for safety or to measure pollutants inthe atmosphere), or other components that may provide indications,measurements, or signals corresponding to a surrounding physicalenvironment. The position components 938 include location sensorcomponents (e.g., a GPS receiver component), altitude sensor components(e.g., altimeters or barometers that detect air pressure from whichaltitude may be derived), orientation sensor components (e.g.,magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 942 further include communication components 940operable to couple the machine 900 to a network 920 or devices 922 via acoupling 924 and a coupling 926, respectively. For example, thecommunication components 940 may include a network interface componentor another suitable device to interface with the network 920. In furtherexamples, the communication components 940 may include wiredcommunication components, wireless communication components, cellularcommunication components, Near Field Communication (NFC) components,Bluetooth® components (e.g., Bluetooth® Low Energy), WiFi® components,and other communication components to provide communication via othermodalities. The devices 922 may be another machine or any of a widevariety of peripheral devices (e.g., a peripheral device coupled via aUSB).

Moreover, the communication components 940 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components 940 may include Radio Frequency Identification(RFID) tag reader components, NFC smart tag detection components,optical reader components (e.g., an optical sensor to detectone-dimensional bar codes such as Universal Product Code (UPC) bar code,multi-dimensional bar codes such as Quick Response (QR) code, Azteccode, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2Dbar code, and other optical codes), or acoustic detection components(e.g., microphones to identify tagged audio signals). In addition, avariety of information may be derived via the communication components940, such as location via Internet Protocol (IP) geolocation, locationvia Wi-Fi® signal triangulation, location via detecting an NFC beaconsignal that may indicate a particular location, and so forth.

The various memories (e.g., memory 904, main memory 912, static memory914, memory of the processors 902), storage unit 916 may store one ormore sets of instructions and data structures (e.g., software) embodyingor used by any one or more of the methodologies or functions describedherein. These instructions (e.g., the instructions 908), when executedby processors 902, cause various operations to implement the disclosedexamples.

The instructions 908 may be transmitted or received over the network920, using a transmission medium, via a network interface device (e.g.,a network interface component included in the communication components940) and using any one of a number of well-known transfer protocols(e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions908 may be transmitted or received using a transmission medium via thecoupling 926 (e.g., a peer-to-peer coupling) to the devices 922.

FIG. 10 is a block diagram 1000 illustrating a software architecture1004, which can be installed on any one or more of the devices describedherein. The software architecture 1004 is supported by hardware such asa machine 1002 that includes processors 1020, memory 1026, and I/Ocomponents 1038. In this example, the software architecture 1004 can beconceptualized as a stack of layers, where each layer provides aparticular functionality. The software architecture 1004 includes layerssuch as an operating system 1012, libraries 1010, frameworks 1008, andapplications 1006. Operationally, the applications 1006 invoke API calls1050 through the software stack and receive messages 1052 in response tothe API calls 1050.

The operating system 1012 manages hardware resources and provides commonservices. The operating system 1012 includes, for example, a kernel1014, services 1016, and drivers 1022. The kernel 1014 acts as anabstraction layer between the hardware and the other software layers.For example, the kernel 1014 provides memory management, processormanagement (e.g., scheduling), component management, networking, andsecurity settings, among other functionality. The services 1016 canprovide other common services for the other software layers. The drivers1022 are responsible for controlling or interfacing with the underlyinghardware. For instance, the drivers 1022 can include display drivers,camera drivers, BLUETOOTH® or BLUETOOTH® Low Energy drivers, flashmemory drivers, serial communication drivers (e.g., Universal Serial Bus(USB) drivers), WI-FI® drivers, audio drivers, power management drivers,and so forth.

The libraries 1010 provide a low-level common infrastructure used by theapplications 1006. The libraries 1010 can include system libraries 1018(e.g., C standard library) that provide functions such as memoryallocation functions, string manipulation functions, mathematicfunctions, and the like. In addition, the libraries 1010 can include APIlibraries 1024 such as media libraries (e.g., libraries to supportpresentation and manipulation of various media formats such as MovingPicture Experts Group-4 (MPEG4), Advanced Video Coding (H.264 or AVC),Moving Picture Experts Group Layer-3 (MP3), Advanced Audio Coding (AAC),Adaptive Multi-Rate (AMR) audio codec, Joint Photographic Experts Group(JPEG or JPG), or Portable Network Graphics (PNG)), graphics libraries(e.g., an OpenGL framework used to render in two dimensions (2D) andthree dimensions (3D) in a graphic content on a display), databaselibraries (e.g., SQLite to provide various relational databasefunctions), web libraries (e.g., WebKit to provide web browsingfunctionality), and the like. The libraries 1010 can also include a widevariety of other libraries 1028 to provide many other APIs to theapplications 1006.

The frameworks 1008 provide a high-level common infrastructure that isused by the applications 1006. For example, the frameworks 1008 providevarious graphical user interface (GUI) functions, high-level resourcemanagement, and high-level location services. The frameworks 1008 canprovide a broad spectrum of other APIs that can be used by theapplications 1006, some of which may be specific to a particularoperating system or platform.

In an example, the applications 1006 may include a home application1036, a contacts application 1030, a browser application 1032, a bookreader application 1034, a location application 1042, a mediaapplication 1044, a messaging application 1046, a game application 1048,and a broad assortment of other applications such as a third-partyapplication 1040. The applications 1006 are programs that executefunctions defined in the programs. Various programming languages can beemployed to create one or more of the applications 1006, structured in avariety of manners, such as object-oriented programming languages (e.g.,Objective-C, Java, or C++) or procedural programming languages (e.g., Cor assembly language). In a specific example, the third-partyapplication 1040 (e.g., an application developed using the ANDROID™ orIOS™ software development kit (SDK) by an entity other than the vendorof the particular platform) may be mobile software running on a mobileoperating system such as IOS™, ANDROID™, WINDOWS® Phone, or anothermobile operating system. In this example, the third-party application1040 can invoke the API calls 1050 provided by the operating system 1012to facilitate functionality described herein.

It will be understood that the terms and expressions used herein havethe ordinary meaning as is accorded to such terms and expressions withrespect to their corresponding respective areas of inquiry and studyexcept where specific meanings have otherwise been set forth herein.Relational terms such as first and second and the like may be usedsolely to distinguish one entity or action from another withoutnecessarily requiring or implying any actual such relationship or orderbetween such entities or actions. The terms “comprises,” “comprising,”“includes,” “including,” or any other variation thereof, are intended tocover a non-exclusive inclusion, such that a process, method, article,or apparatus that comprises or includes a list of elements or steps doesnot include only those elements or steps but may include other elementsor steps not expressly listed or inherent to such process, method,article, or apparatus. An element preceded by “a” or “an” does not,without further constraints, preclude the existence of additionalidentical elements in the process, method, article, or apparatus thatcomprises the element.

Unless otherwise stated, any and all measurements, values, ratings,positions, magnitudes, sizes, and other specifications that are setforth in this specification, including in the claims that follow, areapproximate, not exact. Such amounts are intended to have a reasonablerange that is consistent with the functions to which they relate andwith what is customary in the art to which they pertain. For example,unless expressly stated otherwise, a parameter value or the like,whether or not qualified by a term of degree (e.g., approximate,substantially or about), may vary by as much as ±10% from the recitedamount.

The examples illustrated herein are described in sufficient detail toenable those skilled in the art to practice the teachings disclosed.Other examples may be used and derived therefrom, such that structuraland logical substitutions and changes may be made without departing fromthe scope of this disclosure. The Detailed Description, therefore, isnot to be taken in a limiting sense, and the scope of various examplesis defined only by the appended claims, along with the full range ofequivalents to which such claims are entitled.

What is claimed is:
 1. A system for identifying emerging viewingpatterns for viewed visual media, the system comprising: a visual tagengine configured to identify visual tags for each of a plurality ofvisual media viewed by users; a candidate generator configured togenerate pattern candidates from the identified visual tags, each of thepattern candidates including two or more of the identified visual tags;and a metric evaluation engine configured to evaluate consumptionmetrics of the viewed visual media by the users for each of thegenerated pattern candidates and to identify emerging viewing patternsfor the users responsive to the evaluated consumption metrics.
 2. Thesystem of claim 1, wherein the visual tag engine comprises a processorconfigured to apply a machine learning model to the plurality of visualmedia to identify visual tags.
 3. The system of claim 1, furthercomprising: a taxonomy based filter configured to select a subset of theidentified visual tags; wherein the candidate generator is configured togenerate all combinations of the identified visual tags in the subset ofthe identified visual tags having a number, n, of visual tags; andwherein n is equal or greater than
 2. 4. The system of claim 1, whereinthe metric evaluation engine is configured to: receive one or moreconsumption metrics from each of the users; and score each of thegenerated pattern candidates responsive to the received consumptionmetrics.
 5. The system of claim 4, wherein the viewed visual media arevideos, each of the videos has an associated length of time, and the oneor more consumption metrics includes total viewing time, and, wherein toscore each of the generated pattern candidates, the metric evaluationengine is configured to: calculate a ratio of an average percentage oftime out of the associated length of time that the users viewed each ofthe videos; and aggregate, for each pattern candidate, the ratio of theaverage percentage of time out of the associated length of time that theusers viewed each of the videos.
 6. The system of claim 1, furthercomprising: a display; and a pattern detection system; wherein toidentify the emerging viewing patterns the metric evaluation engine isconfigured to rank the generated pattern candidates responsive to theevaluated consumption metrics; and wherein the pattern detection systemis configured to generate a graph of the ranking of the generatedpattern candidates for visual presentation on the display.
 7. The systemof claim 1, further comprising a display, wherein the users are one of aplurality of groups of users, wherein to identify the emerging viewingpatterns the metric evaluation engine is configured to rank thegenerated pattern candidates responsive to the evaluated consumptionmetrics for each of the groups of users, wherein the users are one of aplurality of groups of users, and wherein the system further comprises:a pattern detection system configured to generate a graph of therankings of the generated pattern candidates for each of the groups ofusers.
 8. The system of claim 1, wherein to evaluate the consumptionmetrics the metric evaluation engine is configured: apply multi-foldcross validation consumption metrics of the viewed visual media by theusers for each of the generated pattern candidates.
 9. A method foridentifying emerging viewing patterns for viewed visual media, themethod comprising: identifying, using a visual tag engine, visual tagsfor each of a plurality of visual media viewed by users; generating,using a candidate generator, pattern candidates from the identifiedvisual tags, each of the pattern candidates including two or more of theidentified visual tags; and evaluating, using a metric evaluationengine, consumption metrics of the viewed visual media by the users foreach of the generated pattern candidates and identifying emergingviewing patterns for the users responsive to the evaluated consumptionmetrics.
 10. The method of claim 9, further comprising: applying amachine learning model, using the visual tag engine, to the plurality ofvisual media to identify visual tags.
 11. The method of claim 9, furthercomprising: selecting a subset of the identified visual tags using ataxonomy based filter; wherein the candidate generator generates allcombinations of the identified visual tags in the subset of theidentified visual tags having a number, n, of visual tags; and wherein nis equal or greater than
 2. 12. The method of claim 9, furthercomprising the metric evaluation engine: receiving one or moreconsumption metrics from each of the users; and scoring each of thegenerated pattern candidates responsive to the received consumptionmetrics.
 13. The method of claim 12, wherein the viewed visual media arevideos, each of the videos has an associated length of time, and the oneor more consumption metrics includes total viewing time, and, whereinthe scoring of each of the generated pattern candidates comprises:calculating a ratio of an average percentage of time out of theassociated length of time that the users viewed each of the videos; andaggregating, for each pattern candidate, the ratio of the averagepercentage of time out of the associated length of time that the usersviewed each of the videos.
 14. The method of claim 9, wherein theidentifying the emerging viewing patterns comprises ranking thegenerated pattern candidates responsive to the evaluated consumptionmetrics and wherein the method further comprises: generating, using apattern detection system, a graph of the ranking of the generatedpattern candidates to visually present on a display.
 15. The method ofclaim 9, wherein the users are one of a plurality of groups of users,wherein the identifying the emerging viewing patterns comprises rankingthe generated pattern candidates responsive to the evaluated consumptionmetrics for each of the groups of users, wherein the users are one of aplurality of groups of users, and wherein the method further comprises:generating, using a pattern detection system, a graph of the rankings ofthe generated pattern candidates for each of the groups of users. 16.The method of claim 9, wherein the evaluating the consumption metricscomprises: applying multi-fold cross validation consumption metrics ofthe viewed visual media by the users for each of the generated patterncandidates.
 17. A non-transitory processor-readable storage mediumstoring processor-executable instructions for identifying emergingviewing patterns for viewed visual media that, when executed by aprocessor of a machine, cause the machine to perform operationscomprising: identifying, using a visual tag engine, visual tags for eachof a plurality of visual media viewed by users; generating, using acandidate generator, pattern candidates from the identified visual tags,each of the pattern candidates including two or more of the identifiedvisual tags; and evaluating, using a metric evaluation engine,consumption metrics of the viewed visual media by the users for each ofthe generated pattern candidates and identifying emerging viewingpatterns for the users responsive to the evaluated consumption metrics.18. The non-transitory processor-readable storage medium of claim 17,wherein the instructions, when executed by a processor of a machine,cause the machine to perform a further operation comprising: selecting asubset of the identified visual tags using a taxonomy based filter;wherein the candidate generator generates all combinations of theidentified visual tags in the subset of the identified visual tagshaving a number, n, of visual tags; and wherein n is equal or greaterthan
 2. 19. The non-transitory processor-readable storage medium ofclaim 17, wherein the evaluating the consumption metrics comprises:receiving one or more consumption metrics from each of the users; andscoring each of the generated pattern candidates responsive to thereceived consumption metrics.
 20. The non-transitory processor-readablestorage medium of claim 19, wherein the visual media are videos, each ofthe videos has an associated length of time, and the one or moreconsumption metrics includes total viewing time, and wherein the scoringcomprises: calculating a ratio of an average percentage of time out ofthe associated length of time that the users viewed each of the videos;and aggregating, for each pattern candidate, the ratio of the averagepercentage of time out of the associated length of time that the usersviewed each of the videos.